Big data hurdles are no different from the common data issues most organizations are facing except on a larger scale. How to jump over these big data hurdles is a different story. With every passing year, technologies are developing and improving to help solve those issues as data sources and volumes increase. Organizations are being overloaded with data from IoT, cameras, websites, social media, documents, data storage, and the list goes on. Big data is meaningless and holds no value without proper analysis that helps organizations gain insight into customer behavior patterns that impacts their business directly. With all the advancements and push towards achieving that goal, sadly big data initiatives still have a success rate of under 50%. Let’s look at those hurdles and how to overcome them.
Big Data Hurdles
Traditional database approaches fail to write fast enough or scale to handle the volume and speed of data being consumed. To add to that, most organizations struggle to structure or architect a solution that can handle this amount of data to deliver beneficial analysis and visualizations. This is mostly due to the lack of experienced and knowledgeable data scientists. With data coming from so many disparate sources, data security becomes an issue not to mention the amount of storage needed to house all that data.
Too Much Data
Big data requires a tremendous amount of storage and the ability to retrieve data quickly. Fortunately, costs have been dropping with new technologies increasing data compression and the need for less storage. Various technologies such as flash-based storage arrays, software-defined storage, and hyper-converged infrastructure allow for hardware scaling and speedy data retrieval. If the time to result is high (as in hours to days) then organizations dealing with real-time data can miss crucial analysis that impacts their business and may save them in the millions of dollars. Even with all the advances in technology, most organizations are struggling to deal with big data on-premise. Moving the data processing to the cloud with applications centered around Machine Learning and advanced analytics helps achieve significantly better results with less involvement in management and more focus on analysis.
Analyzing All That Data
Analyzing and validating such large and complex amounts of data is no easy task. Big data comes from unstructured & structured sources in different formats causing inconsistent data, missing data, logic conflicts, duplication, and other scenarios. One of the major hurdles faced by most organizations is synchronizing the data from all those disparate sources for analysis. It takes careful planning and well-designed architecture. This creates the need for skilled, knowledgeable and seasoned data scientists and architects, which are quite rare to find. There are tools available to aid in the process but again finding people with the skill-sets or familiarity with the new technologies is rare. So, if you find a good data scientist, grab on to them, pay them well and keep them happy.
Securing and Governing Data
When big data is flowing in from so many different sources, security becomes a major concern. This brings in the need for data governance and other tools to help secure the data and only allow parties privy to the data to view it. There are many tools out there to help organizations achieve this goal. Blockchain technology is proving to be very effective in responding to those challenges. It creates a single, unchangeable resource for the company which in turn enhances security and data integrity.
Blockchain is decentralized, which allows it to handle large volumes of data. It also requires users to complete multiple authorizations from various parts of the network before gaining access to the data. This makes it easier to securely share records with all stakeholders involved. A corruption of data in any part of the blockchain must be validated before integration into the network; therefore maintaining data integrity. It’s a great solution, however, very few people have that expertise as it’s a fairly new technology.
Making sense of big data to help organizations save or make money is no easy task. It requires massive scalable and speedy storage, data management to create impactful analysis, familiarity and keeping up with new technologies, data security and governance, and skilled hires such as data scientists/architects that can help you plan and make sense of all that data. Most organizations are finding it easier to rely on large companies that specialize in all these areas and can provide expertise & technology. Consider looking at companies that are leaders in big data who can provide the support and necessary services to aid with your organization’s data. Such companies that are leading in this domain are Teradata, IBM, Amazon, and HP Enterprise to name a few. Don’t let big data be intimidating!
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